![PyTorch Ultimate 2023 - From Basics to Cutting-Edge [Video]](https://content.packt.com/V21664/cover_image_small.jpg)
PyTorch Ultimate 2023 - From Basics to Cutting-Edge [Video]
Subscription
FREE
Video + Subscription
$29.99
Video
$89.99
What do you get with a Packt Subscription?
What do you get with a Packt Subscription?
What do you get with Video + Subscription?
What do you get with a Packt Subscription?
What do you get with eBook?
What do I get with Print?
What do I get with Print?
What do you get with video?
What do you get with Audiobook?
Subscription
FREE
Video + Subscription
$29.99
Video
$89.99
What do you get with a Packt Subscription?
What do you get with a Packt Subscription?
What do you get with Video + Subscription?
What do you get with a Packt Subscription?
What do you get with eBook?
What do I get with Print?
What do I get with Print?
What do you get with video?
What do you get with Audiobook?
-
Free ChapterCourse Overview and System Setup
-
Machine Learning
-
Deep Learning Introduction
-
Model Evaluation
-
Neural Network from Scratch
- Section Overview
- Neural Network from Scratch (101)
- Calculating the dot-product (Coding)
- Neural Network from Scratch (Data Prep)
- Neural Network from Scratch Modeling __init__ Function
- Neural Network from Scratch Modeling Helper Functions
- Neural Network from Scratch Modeling Forward Function
- Neural Network from Scratch Modeling Backward Function
- Neural Network from Scratch Modeling Optimizer Function
- Neural Network from Scratch Modeling Train Function
- Neural Network from Scratch Model Training
- Neural Network from Scratch Model Evaluation
-
Tensors
-
PyTorch Modeling Introduction
- Section Overview
- Linear Regression from Scratch (Coding, Model Training)
- Linear Regression from Scratch (Coding, Model Evaluation)
- Model Class (Coding)
- Exercise: Learning Rate and Number of Epochs
- Solution: Learning Rate and Number of Epochs
- Batches (101)
- Batches (Coding)
- Datasets and Dataloaders (101)
- Datasets and Dataloaders (Coding)
- Saving and Loading Models (101)
- Saving and Loading Models (Coding)
- Model Training (101)
- Hyperparameter Tuning (101)
- Hyperparameter Tuning (Coding)
-
Classification Models
- Section Overview
- Classification Types (101)
- Confusion Matrix (101)
- ROC Curve (101)
- Multi-Class 1: Data Prep
- Multi-Class 2: Dataset Class (Exercise)
- Multi-Class 3: Dataset Class (Solution)
- Multi-Class 4: Network Class (Exercise)
- Multi-Class 5: Network Class (Solution)
- Multi-Class 6: Loss, Optimizer, and Hyperparameters
- Multi-Class 7: Training Loop
- Multi-Class 8: Model Evaluation
- Multi-Class 9: Naive Classifier
- Multi-Class 10: Summary
- Multi-Label (Exercise)
- Multi-Label (Solution)
-
CNN: Image Classification
- Section Overview
- CNNs (101)
- CNN (Interactive)
- Image Preprocessing (101)
- Image Preprocessing (Coding)
- Binary Image Classification (101)
- Binary Image Classification (Coding)
- Multi-Class Image Classification (Exercise)
- Multi-Class Image Classification (Solution)
- Layer Calculations (101)
- Layer Calculations (Coding)
-
CNN: Audio Classification
-
CNN: Object Detection
- Section Overview
- Accuracy Metrics (101)
- Object Detection (101)
- Object Detection with detecto (Coding)
- Training a Model on GPU for Free (Coding)
- YOLO (101)
- Labeling Formats
- YOLOv7 Project (101)
- YOLOv7 Coding: Setup
- YOLOv7 Coding: Data Prep
- YOLOv7 Coding: Model Training
- YOLOv7 Coding: Model Inference
- YOLOv8 Coding: Model Training and Inference
-
Style Transfer
-
Pre-Trained Networks and Transfer Learning
-
Recurrent Neural Networks
-
Recommender Systems
-
Autoencoders
-
Generative Adversarial Networks
-
Graph Neural Networks
-
Transformers
-
PyTorch Lightning
-
Semi-Supervised Learning
-
Natural Language Processing (NLP)
- Natural Language Processing (101)
- Word Embeddings Intro (101)
- Sentiment OHE Coding Introduction
- Sentiment OHE (Coding)
- Word Embeddings with Neural Network (101)
- GloVe: Get Word Embedding (Coding)
- Glove: Find st Words (Coding)
- GloVe: Word Analogy (Coding)
- GloVe Word Cluster (101)
- GloVe Word (Coding)
- Sentiment with Embedding (101)
- Sentiment with Embedding (Coding)
- Apply Pre-Trained Natural Language Processing Models (101)
- Apply Pre-Trained Natural Language Processing Models (Coding)
-
Miscellaneous Topics
-
Model Debugging
-
Model Deployment
-
Final Section
About this video
PyTorch is a Python framework developed by Facebook to develop and deploy deep learning models. It is one of the most popular deep-learning frameworks nowadays.
You will begin with learning the deep learning concept. Dive deeper into tensor handling, acquiring the finesse to create and manipulate tensors while leveraging PyTorch’s automatic gradient calculation through Autograd. Then transition to modeling by constructing linear regression models from scratch. After that, you will dive deep into classification models, mastering both multilabel and multiclass. You will then see the theory behind object detection and acquire the prowess to build object detection models. Embrace the cutting edge with YOLO v7, YOLO v8, and faster RCNN, and unleash the potential of pre-trained models and transfer learning.
Delve into RNNs and look at recommender systems, unlocking matrix factorization techniques to provide personalized recommendations. Refine your skills in model debugging and deployment, where you will debug models using hooks, and navigate the strategies for both on-premise and cloud deployment. Finally, you will explore ChatGPT, ResNet, and Extreme Learning Machines.
By the end of this course, you will have learned the key concepts, models, and techniques, and have the confidence to craft and deploy robust deep-learning solutions.
- Publication date:
- September 2023
- Publisher
- Packt
- Duration
- 17 hours 36 minutes
- ISBN
- 9781801070089